



from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_snake_case						=    200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
_snake_case						=    50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
_snake_case						=    0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))






def 							A							(   _lowerCamelCase							,					_lowerCamelCase    ):





							'''simple docstring'''




							_lowerCAmelCase	:				List[str]					  =       len([g for position, g in enumerate(a_    ) if g == main_target[position]]    )
							return (item, float(a_    ))






def 							A							(   _lowerCamelCase							,					_lowerCamelCase    ):





							'''simple docstring'''




							_lowerCAmelCase	:				Tuple					  =       random.randint(0							,					len(a_    ) - 1    )
							_lowerCAmelCase	:				Optional[Any]					  =       parent_a[:random_slice] + parent_a[random_slice:]
							_lowerCAmelCase	:				Optional[Any]					  =       parent_a[:random_slice] + parent_a[random_slice:]
							return (child_a, child_a)






def 							A							(   _lowerCamelCase							,					_lowerCamelCase    ):





							'''simple docstring'''




							_lowerCAmelCase	:				Dict					  =       list(a_    )
							if random.uniform(0							,					1    ) < MUTATION_PROBABILITY:
														_lowerCAmelCase	:				List[Any]					  =       random.choice(a_    )
							return "".join(a_    )






def 							A							(   _lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase							,					):





							'''simple docstring'''




							_lowerCAmelCase	:				Union[str, Any]					  =       []
							# Generate more children proportionally to the fitness score.
							_lowerCAmelCase	:				List[Any]					  =       int(parent_a[1] * 100    ) + 1
							_lowerCAmelCase	:				str					  =       10 if child_n >= 10 else child_n
							for _ in range(a_    ):
														_lowerCAmelCase	:				Dict					  =       population_score[random.randint(0							,					a_    )][0]

														_lowerCAmelCase	:				Dict					  =       crossover(parent_a[0]							,					a_    )
														# Append new string to the population list.
														pop.append(mutate(a_							,					a_    )    )
														pop.append(mutate(a_							,					a_    )    )
							return pop






def 							A							(   _lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase = True    ):





							'''simple docstring'''




							if N_POPULATION < N_SELECTED:
														_lowerCAmelCase	:				List[Any]					  =       F"{N_POPULATION} must be bigger than {N_SELECTED}"
														raise ValueError(a_    )
							# Verify that the target contains no genes besides the ones inside genes variable.
							_lowerCAmelCase	:				Any					  =       sorted({c for c in target if c not in genes}    )
							if not_in_genes_list:
														_lowerCAmelCase	:				Optional[int]					  =       F"{not_in_genes_list} is not in genes list, evolution cannot converge"
														raise ValueError(a_    )

							# Generate random starting population.
							_lowerCAmelCase	:				int					  =       []
							for _ in range(a_    ):
														population.append("".join([random.choice(a_    ) for i in range(len(a_    )    )]    )    )

							# Just some logs to know what the algorithms is doing.
							_lowerCAmelCase	:				int					  =       0, 0

							# This loop will end when we find a perfect match for our target.
							while True:
														generation += 1
														total_population += len(a_    )

														# Random population created. Now it's time to evaluate.

														# Adding a bit of concurrency can make everything faster,
														#
														# import concurrent.futures
														# population_score: list[tuple[str, float]] = []
														# with concurrent.futures.ThreadPoolExecutor(
														#                                   max_workers=NUM_WORKERS) as executor:
														#     futures = {executor.submit(evaluate, item) for item in population}
														#     concurrent.futures.wait(futures)
														#     population_score = [item.result() for item in futures]
														#
														# but with a simple algorithm like this, it will probably be slower.
														# We just need to call evaluate for every item inside the population.
														_lowerCAmelCase	:				Optional[int]					  =       [evaluate(a_							,					a_    ) for item in population]

														# Check if there is a matching evolution.
														_lowerCAmelCase	:				str					  =       sorted(a_							,					key=lambda _lowerCamelCase    : x[1]							,					reverse=a_    )
														if population_score[0][0] == target:
																					return (generation, total_population, population_score[0][0])

														# Print the best result every 10 generation.
														# Just to know that the algorithm is working.
														if debug and generation % 10 == 0:
																					print(
																					    F"\nGeneration: {generation}"
																					    F"\nTotal Population:{total_population}"
																					    F"\nBest score: {population_score[0][1]}"
																					    F"\nBest string: {population_score[0][0]}"    )

														# Flush the old population, keeping some of the best evolutions.
														# Keeping this avoid regression of evolution.
														_lowerCAmelCase	:				Any					  =       population[: int(N_POPULATION / 3    )]
														population.clear()
														population.extend(a_    )
														# Normalize population score to be between 0 and 1.
														_lowerCAmelCase	:				Union[str, Any]					  =       [
														    (item, score / len(a_    )) for item, score in population_score
														]

														# This is selection
														for i in range(a_    ):
																					population.extend(select(population_score[int(a_    )]							,					a_							,					a_    )    )
																					# Check if the population has already reached the maximum value and if so,
																					# break the cycle.  If this check is disabled, the algorithm will take
																					# forever to compute large strings, but will also calculate small strings in
																					# a far fewer generations.
																					if len(a_    ) > N_POPULATION:
																												break


if __name__ == "__main__":
						_snake_case						=    (
						    "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
						)
						_snake_case						=    list(
						    " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
						    "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
						)
						_snake_case,		_snake_case,		_snake_case						=    basic(target_str, genes_list)
						print(
						    f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
						)







'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   list[tuple[int, int]]

lowerCAmelCase									=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCAmelCase									=   ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_		,   ):
			lowerCAmelCase  :     int       						= pos_x
			lowerCAmelCase  :     Union[str, Any]       						= pos_y
			lowerCAmelCase  :     Optional[Any]       						= (pos_y, pos_x)
			lowerCAmelCase  :     Union[str, Any]       						= goal_x
			lowerCAmelCase  :     Tuple       						= goal_y
			lowerCAmelCase  :     str       						= g_cost
			lowerCAmelCase  :     Dict       						= parent
			lowerCAmelCase  :     List[Any]       						= self.calculate_heuristic()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= abs(self.pos_x - self.goal_x						)
			lowerCAmelCase  :     Any       						= abs(self.pos_y - self.goal_y						)
			return dx + dy





	def __lt__( self		,   a_						):
			return self.f_cost < other.f_cost






class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= Node(start[1]		,   start[0]		,   goal[1]		,   goal[0]		,   0		,   a_						)
			lowerCAmelCase  :     Union[str, Any]       						= Node(goal[1]		,   goal[0]		,   goal[1]		,   goal[0]		,   99_999		,   a_						)

			lowerCAmelCase  :     Tuple       						= [self.start]
			lowerCAmelCase  :     list[Node]       						= []

			lowerCAmelCase  :     Tuple       						= False



	def 	_lowerCamelCase     ( self						):
			while self.open_nodes:
					# Open Nodes are sorted using __lt__
					self.open_nodes.sort()
					lowerCAmelCase  :     List[Any]       						= self.open_nodes.pop(0						)

					if current_node.pos == self.target.pos:
							lowerCAmelCase  :     Tuple       						= True
							return self.retrace_path(a_						)

					self.closed_nodes.append(a_						)
					lowerCAmelCase  :     Tuple       						= self.get_successors(a_						)

					for child_node in successors:
							if child_node in self.closed_nodes:
									continue

							if child_node not in self.open_nodes:
									self.open_nodes.append(a_						)
							else:
									# retrieve the best current path
									lowerCAmelCase  :     int       						= self.open_nodes.pop(self.open_nodes.index(a_						)						)

									if child_node.g_cost < better_node.g_cost:
											self.open_nodes.append(a_						)
									else:
											self.open_nodes.append(a_						)

			if not self.reached:
					return [self.start.pos]
			return None



	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= []
			for action in delta:
					lowerCAmelCase  :     Dict       						= parent.pos_x + action[1]
					lowerCAmelCase  :     str       						= parent.pos_y + action[0]

					if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(a_						) - 1):
							continue

					if grid[pos_y][pos_x] != 0:
							continue

					successors.append(
					    Node(
					        a_		,   a_		,   self.target.pos_y		,   self.target.pos_x		,   parent.g_cost + 1		,   a_		,   )						)
			return successors





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= node
			lowerCAmelCase  :     List[str]       						= []
			while current_node is not None:
					path.append((current_node.pos_y, current_node.pos_x)						)
					lowerCAmelCase  :     Dict       						= current_node.parent
			path.reverse()
			return path


if __name__ == "__main__":
					lowerCAmelCase									=   (0, 0)
					lowerCAmelCase									=   (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("""------""")

					lowerCAmelCase									=   GreedyBestFirst(init, goal)
					lowerCAmelCase									=   greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															lowerCAmelCase									=   2

										for elem in grid:
															print(elem)



